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The aim of this study is to build an evaluation framework for the user-centric testing of the Data Curation Tool. The tool was developed in the scope of the FAIR4Health project to make health data FAIR by transforming them from legacy formats into a Common Data Model based on HL7 FHIR. The end user evaluation framework was built by following a methodology inspired from the Delphi method. We applied a series of questionnaires to a group of experts not only in different roles and skills, but also from various parts of Europe. Overall, 26 questions were formulated for 16 participants. The results showed that the users are satisfied with the capabilities and performance of the tool. The feedbacks were considered as recommendations for technical improvement and fed back into the software development cycle of the Data Curation Tool.

[Background] The evidence-based medicine (EBM) paradigm requires the development of health care professionals’ skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients’ health care, increase patient safety, and optimize resources use., [Objective] The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice., [Methods] Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy., [Results] The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times., [Conclusions] The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future., This project has received funding from the Andalusian Ministry of Health from Spain (reference PIN-0213-2016), and FEDER funds.

Background: Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients., Methods: De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts., Findings: The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-ofsample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84- 0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use., Interpretation: The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States., HW is supported by the National Science Foundation Graduate Research Fellowship under Grant No. 174530.

[Background] FAIR (findability, accessibility, interoperability, and reusability) guiding principles seek the reuse of data and other digital research input, output, and objects (algorithms, tools, and workflows that led to that data) making them findable, accessible, interoperable, and reusable. GO FAIR - a bottom-up, stakeholder driven and self-governed initiative - defined a seven-step FAIRification process focusing on data, but also indicating the required work for metadata. This FAIRification process aims at addressing the translation of raw datasets into FAIR datasets in a general way, without considering specific requirements and challenges that may arise when dealing with some particular types of data., [Objectives] This scientific contribution addresses the architecture design of an open technological solution built upon the FAIRification process proposed by “GO FAIR” which addresses the identified gaps that such process has when dealing with health datasets., [Methods] A common FAIRification workflow was developed by applying restrictions on existing steps and introducing new steps for specific requirements of health data. These requirements have been elicited after analyzing the FAIRification workflow from different perspectives: technical barriers, ethical implications, and legal framework. This analysis identified gaps when applying the FAIRification process proposed by GO FAIR to health research data management in terms of data curation, validation, deidentification, versioning, and indexing., [Results] A technological architecture based on the use of Health Level Seven International (HL7) FHIR (fast health care interoperability resources) resources is proposed to support the revised FAIRification workflow., [Discussion] Research funding agencies all over the world increasingly demand the application of the FAIR guiding principles to health research output. Existing tools do not fully address the identified needs for health data management. Therefore, researchers may benefit in the coming years from a common framework that supports the proposed FAIRification workflow applied to health datasets., [Conclusion] Routine health care datasets or data resulting from health research can be FAIRified, shared and reused within the health research community following the proposed FAIRification workflow and implementing technical architecture., This work was performed in the scope of FAIR4Health project31. FAIR4Health has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666.

[Background]: Telemedicine technologies have a valuable potential when it comes to improving the accuracy of triage protocols in selecting severely injured patients who may benefit from transportation. The main objective of this study was to evaluate the correlation of the urgent diagnosis made by telemedicine through an App with traditional face-to-face urgent care and the final diagnosis, made by scheduled consultation., [Methods]: We carried out a descriptive cross-sectional study to evaluate the accuracy of telemedicine in burns evaluation compared to gold standard (in-person consultation). An App was designed. All patients enrolled were evaluated by both teleconsultation and face-to-face approach on burn emergencies. Diagnosis on presentation made by a physician constituted the gold standard., [Results]: A total of 202 patients were included in the study. The use of TM was able to detect that 83.17% of the patients attending the BU could have been managed on as outpatient basis. The intra-observer concordance was k = 0.94 (95% CI: 0.90–0.97). These results highlight a very high sensitivity and specificity (99.40 and 100% respectively)., [Conclusions]: The telemedicine system for planning referrals is a useful tool that may make significant differences in the management of burned patients although further research needs to be taken in that direction.

Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable., PK received speaker honoraria from Aflofarm, Fresenius, Polpharma, and Sandoz and received grant funding from European Union’s Horizon 2020 for GATEKEEPER project (grant agreement number 857223) and The European Commission ERASMUS+ Project Skills4Adherence (grant agreement number: 2017-1-PL01-KA202-038672), outside of this work. BV is the CEO and shareholder of AARDEX Group.

[Background and goal] Health information systems are increasingly sophisticated and developing them is a challenge for software developers. Software engineers usually make use of UML as a standard model language that allows defining health information system entities and their relations. However, working with health system requires learning HL7 standards, that defines and manages standards related to health information systems. HL7 standards are varied, however this work focusses on v2 and v3 since these are the most used one on the area that this work is being conducted. This works aims to allow modeling HL7 standard by using UML., [Methods] Several techniques based on the MDE (Model-Driven Engineering) paradigm have been used to cope with it., [Results] A useful reference framework, reducing final users learning curve and allowing modeling maintainable and easy-going health information systems., [Conclusions] By using this approach, a software engineer without any previous knowledge about HL7 would be able to solve the problem of modeling HL7-based health information systems. Reducing the learning curve when working in projects that need HL7 standards., This research has been partially supported by the POLOLAS project (code TIN2016–76956-C3–2-R) of the Spanish Ministry of Science and Innovation, the KNOWBED project (code PIN-0213–2016) founded by the Andalusian Ministry of Health, Carlos III Health Institute within the call Strategic Help in Health (PITeS TIiSS project, code PI15/01213), by the VPPI of the University of Seville, and FEDER funds. The authors are grateful to Carlos III Health Institute for promoting the Network for Innovation in Medical Technologies and Health (“ITEMAS” in Spanish, CODE PT17/0005/0032), and Prevensalud project founded by the Technological Corporation of Andalusia.

[EN]: The digital era, that we are living nowadays, is transforming health, health care models and services, and the role of society in this new reality. We currently have a large amount of stored health data, including clinical, biometric, and scientific research data. Nonetheless, its potential is not being fully exploited. It is essential to foster the sharing and reuse of this data not only in research but also towards the development of health technologies in order to improve health care efficiency, as well as products, services or digital health apps, to promote preventive and individualized medicine and to empower citizens in health literacy and self-management. In this sense, the FAIR concept has emerged, which implies that health data is findable, accessible, shared and reusable, facilitating interoperability between systems, ensuring the protection of personal and sensitive data. In this paper we review the FAIR concept, ‘FAIRification’ process, FAIR data versus open access data, ethical issues and the general data protection regulation, and digital health and citizen science., [PT]: Vivemos uma nova era digital que está a transformar a saúde, os modelos de cuidados e serviços de saúde, e o próprio papel da sociedade nesta realidade. Atualmente dispomos de uma grande quantidade de dados de saúde armazenados, incluindo dados clínicos, biométricos e de investigação científica, cuja potencialidade não está a ser devidamente explorada. É essencial favorecer a partilha e reutilização destes dados não só na investigação, como também para o desenvolvimento de tecnologias para melhorar a eficiência dos cuidados de saúde, de produtos ou serviços de saúde digitais, promover uma medicina preventiva e individualizada, mas também o empoderamento da população em literacia em saúde e na gestão da doença. Recentemente, surgiu o conceito FAIR que implica que os dados de saúde sejam facilmente localizáveis, acessíveis, partilhados e reutilizáveis, facilitando desta forma a interoperacionalidade entre sistemas e assegurando a proteção de dados pessoais e sensíveis. Neste artigo é feita uma revisão do conceito FAIR, processo de ‘FAIRificação’, dados FAIR versus dados de acesso livre, questões de éticas e o regulamento geral de proteção de dados, e saúde digital e ciência cidadã., Este trabalho foi financiado pelo Projeto Europeu FAIR4Health (EU H2020/No 824666) e pela FCT/MCTES através de financiamento público (UIDB/04378/2020).

Introduction and Objectives: The main objective is to analyze the impact of incorporating a mobile application to the process of smoking cessation in terms of efficacy after one-year follow-up. Methods: The trial takes place within the SmokeFreenBrain European project, financed by the H2020 program under GA No 681120 and registered in Clinicaltrials.govNCT03553173, performed from October 2017 to October 2018. 240 patients from the Smoking Cessation Office were recruited. Randomized open trial with 4 parallel groups (2 control groups - bupropion or varenicline - and 2 intervention groups - bupropion + app or varenicline + app). Data were collected regarding epidemiology, clinical features, Fagerstrom test, Richmond test, EuroQoL-5D-5L quality of life test, co-oximetry and cotinine test. Results: 97 out of 240 patients recruited completed the 12-month follow-up. The sample was made up of 49 men and 46 women, with a mean (±SD) age of 50.3 ± 9.08. Out of 97 patients, 46.5% were assigned to the control group and 53.7% to the intervention group. Fagerstrom test showed a nicotine dependence of 5.2 ± 2.1 and 5.1 ± 1.8 in the control group and the intervention group, respectively. Richmond test assessed the motivation to quit smoking, measuring 9.4 ± 0.8 in the control group and 9.4 ± 0.8 in the intervention group. Efficacy (abstinence after a 12-month follow-up) was of 64.7% in the intervention group and of 40.9% in the control group (p=0.002), leaving aside the pharmacological treatment prescribed. Conclusions: Using a motivational mobile phone App together with pharmacological treatment is significantly more effective in achieving smoking cessation after a 12-month follow-up, compared to pharmacological treatment alone.

Background: Smoking is considered the main cause of preventable illness and early deaths worldwide. The treatment usually prescribed to people who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt. Objective: We present a study protocol of a 12-month randomized open-label parallel-group trial whose primary objective is to analyze the efficacy and efficiency of usual psychopharmacological therapy plus the Social-Local-Mobile app (intervention group) applied to the smoking cessation process compared with usual psychopharmacological therapy alone (control group). Methods: The target population consists of adult smokers (both male and female) attending the Smoking Cessation Unit at Virgen del Rocio University Hospital, Seville, Spain. Social-Local-Mobile is an innovative intervention based on mobile technologies and their capacity to trigger behavioral changes. The app is a complement to pharmacological therapies to quit smoking by providing personalized motivational messages, physical activity monitoring, lifestyle advice, and distractions (minigames) to help overcome cravings. Usual pharmacological therapy consists of bupropion (Zyntabac 150 mg) or varenicline (Champix 0.5 mg or 1 mg). The main outcomes will be (1) the smoking abstinence rate at 1 year measured by means of exhaled carbon monoxide and urinary cotinine tests, and (2) the result of the cost-effectiveness analysis, which will be expressed in terms of an incremental cost-effectiveness ratio. Secondary outcome measures will be (1) analysis of the safety of pharmacological therapy, (2) analysis of the health-related quality of life of patients, and (3) monitoring of healthy lifestyle and physical exercise habits. Results: Of 548 patients identified using the hospital’s electronic records system, we excluded 308 patients: 188 declined to participate and 120 did not meet the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive usual psychopharmacological therapy, while the intervention group (n=120) will receive usual psychopharmacological therapy plus the So-Lo-Mo app. The project was approved for funding in June 2015. Enrollment started in October 2016 and was completed in October 2017. Data gathering was completed in November 2018, and data analysis is under way. The first results are expected to be submitted for publication in early 2019. Conclusions: Social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking habits as well. As part of the SmokeFreeBrain H2020 European Commission project, this study aims at elucidating the potential role of these technologies when used as an extra aid to quit smoking. Trial Registration: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/record/NCT03553173 (Archived by WebCite at http://www.webcitation.org/74DuHypOW). International Registered Report Identifier (IRRID): PRR1-10.2196/12464

[Background] Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies., [Objective] This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology., [Methods] A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence–generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial., [Results] In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study., [Conclusions] The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence–based future approaches., [Trial Registration] ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/NCT03553173, [International Registered Report Identifier (IRRID)] RR2-10.2196/12464., This research was funded by the H2020 European Commission research and innovation program (grant agreement 681120) as part of the SmokeFreeBrain project (www.smokefreebrain.eu).

[Background] The Platform for Innovation in Medical and Health Technologies (ITEMAS) is a network of 66 healthcare centres focused on fostering innovation in medical and health technologies as an essential tool for increasing the sustainability of the Spanish healthcare system. The present research is focused on defining a formal representation that details the most relevant concepts associated with the creation and adoption of innovative medical technology in the Spanish healthcare system., [Methods] The methodology applied is based on the methontology process, including peer-review identification and selection of concepts from the ITEMAS innovation indicators and innovation management system standards. This stage was followed by an iterative validation process. Concepts were then conceptualised, formalised and implemented in an ontology., [Results] The ontology defined describes how relationships between employees, organisations, projects and ideas can be applied to generate results that are transferrable to the market, general public and scientific forums. Overall, we identified 136 concepts, 138 object properties and 30 properties in a five-level hierarchy. The ontology was tested and validated as an appropriate framework for calculating the ITEMAS innovation indicators., [Conclusions] The consensus concepts were expressed in the form of an ontology to be used as a single communication format between the members of the ITEMAS network. Healthcare centres can compare their innovation results and obtain a better understanding of their innovation context based on the reasoning techniques of artificial intelligence. As a result, they can benefit from advanced analytical capabilities to define the most appropriate innovation policies for each centre based on the common experience of the large number of healthcare centres involved. The results can be used to create a map of agents and knowledge to show capabilities, projects and services provided by each of the participating centres. The ontology could also be applied as an instrument to match needs with existing projects and capabilities from the community of organisations working in healthcare technology innovation., This research has been partly funded by the ITEMAS Platform (PT13/0006/0001, PT13/0006/0036 and PT17/0005/0036) by the Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, both co-funded by European Regional Development Fund (ERDF), “A way of making Europe”.

Introduction Social-Local-Mobile (So-Lo-Mo) is an innovative intervention based on mobile technologies: it has the capacity to trigger behavioural changes. In this sense, the So-Lo-Mo App is a complement to pharmacological therapies to quit smoking providing personalised motivational messages, physical activity monitoring, lifestyle advices and distractions (mini-games) to help pass the cravings. The purpose of this study is to analyse the efficacy of the So-Lo-Mo intervention applied to the smoking cessation process compared to usual care. Secondary study objectives are: (i) to analyse safety of pharmacological therapy; (ii) to analyse subjects’ motivation to give up smoking; (iii) to analyse subjects’ health-related quality of life; (iv) to monitor physical activity; and (v) to monitor healthy lifestyle habits. Methods So-Lo-Mo study is a 12-months randomized open-label parallel-group trial performance at the Virgen del Rocio University Hospital premises. Efficacy was considered as the abstinence rate at 1 year measured by means of exhaled Carbon Monoxide (CO) and urine cotinine tests; safety was measured as the number of adverse events related with pharmacological therapies under consideration (either bupropion or varenicline); subjects’ motivation was measured through the Richmond test; health-related quality of life was measured through the EuroQoL-5D-5L questionnaire; physical activity was measured through the International Physical Activity Questionnaire (IPAQ-27); and lifestyle habits was measured through the subjects’ weight evolution during the follow-up period. Results So far, data gathered from 59 subjects (30 women, 28 controls) has been analysed at 6 months form baseline, yielding the following results: (i) Efficacy: success rate were 53.6% and 71% for control and intervention groups respectively (p = 0.168). (ii) Safety: 12 adverse events were reported for the bupropion therapy (n = 24) while 30 adverse events were reported for the varenicline therapy (n = 35). (iii) Motivation: Richmond scores were 9.36 ± 0.83 and 9.50 ± 0.63 (p = 0.465) for control and intervention group respectively. (iv) Health-related quality of life: the average evolution on the Visual Analogic Scale (VAS) score was 6.87 ± 11.31 (p = 0.007) and 7.69 ± 16.20 (p = 0.023) for control and intervention groups respectively. (v) Physical activity: the evolution of metabolic equivalent (MET) minutes per week was 1120.42 ± 6113.12 and -10.36 ± 4188.47 (p = 0.436) for control and intervention groups respectively. (vi) Lifestyle habits: subjects’ weight evolution in kilograms was 3.51 ± 2.90 and 2.93 ± 5.77 (p = 0.202) for control and intervention groups respectively. Conclusions (i) Efficacy: the So-Lo-Mo intervention provides a consistent improvement on the efficacy compared to controls. (ii) Safety: the number and type of the reported adverse events related to the pharmacological treatment is in keeping to their expected incidence as reported by the European Medicines Agency. (iii) Motivation: a slight bias on the subjects’ motivation has been reported, showing lower motivation score in controls compared to intervention group. (iv) Health-related quality of life: Statistically significant improvements in the VAS health score were found for both groups. Furthermore, a greater average improvement has been reported for intervention subjects compared to controls, which suggest that the intervention has an added value. (v) Physical activity: physical activity evolution has been reported higher for controls compared to intervention subjects. This finding should be taken with care, since the variability reported for the increments in the IPAQ27 score is very high and, therefore, more observations are needed before drawing a conclusion from this analysis. (vi) Lifestyle habits: weight increment in control group was reported to be higher when compared to intervention group. This finding, in combination with the efficacy, could support the preliminary conclusion that the So-Lo-Mo intervention improves the efficacy of the smoking cessation process while helps to control weight increments associated to the nicotine abstinence. Preliminary analyses show a very promising trend in achieving the main objective of this study. However, stronger statistical significance is needed to endorse this statement.

Display Omitted The new domain models exploitation paradigm: Model Driven Engineering.The challenge of use the HL7 metamodel in the MDE context.Software engineers will use the benefits of HL7 standards and UML & MDE existing tools. HL7 (Health Level 7) International is an organization that defines health information standards. Most HL7 domain information models have been designed according to a proprietary graphic language whose domain models are based on the HL7 metamodel. Many researchers have considered using HL7 in the MDE (Model-Driven Engineering) context. A limitation has been identified: all MDE tools support UML (Unified Modeling Language), which is a standard model language, but most do not support the HL7 proprietary model language. We want to support software engineers without HL7 experience, thus real-world problems would be modeled by them by defining system requirements in UML that are compliant with HL7 domain models transparently. The objective of the present research is to connect HL7 with software analysis using a generic model-based approach. This paper introduces a first approach to an HL7 MDE solution that considers the MIF (Model Interchange Format) metamodel proposed by HL7 by making use of a plug-in developed in the EA (Enterprise Architect) tool.

Background New technologies, such as telerehabilitation and gaming devices offer the possibility for patients to train at home. This opens the challenge of safety for the patient as he/she is called to exercise neither with a therapist on the patients' side nor with a therapist linked remotely to supervise the sessions. Aim To study the safety, usability and patient acceptance of an autonomous telerehabilitation system for balance and gait (the REWIRE platform) in the patients home. Design Cohort study. Setting Community, in the stroke patients' home. Population Fifteen participants with first-ever stroke, with a mild to moderate residual deficit of the lower extremities. Methods Autonomous rehabilitation based on virtual rehabilitation was provided at the participants' home for twelve weeks. The primary outcome was compliance (the ratio between days of actual and scheduled training), analyzed with the two-tailed Wilcoxon Mann-Whitney test. Furthermore safety is defined by adverse events. The secondary endpoint was the acceptance of the system measured with the Technology Acceptance Model (TAM). Additionally, the cumulative duration of weekly training was analyzed. Results During the study there were no adverse events related to the therapy. Patients performed on average 71% (range 39 to 92%) of the scheduled sessions. The TAM Questionnaire showed excellent values for stroke patients after the training. The average training duration per week was 99±53min. Conclusions Autonomous telerehabilitation for balance and gait training with the REWIRE-system is safe, feasible and can help to intensive rehabilitative therapy at home. Clinical rehabilitation impact Telerehabilitation enables safe training in home environment and supports of the standard rehabilitation therapy.

In 2005, an application for surgical planning called AYRA $${\textregistered }$$ was designed and validated by different surgeons and engineers at the Virgen del Rocio University Hospital, Seville (Spain). However, the segmentation methods included in AYRA and in other surgical planning applications are not able to segment accurately tumors that appear in soft tissue. The aims of this paper are to offer an exhaustive validation of an accurate semiautomatic segmentation tool to delimitate retroperitoneal tumors from CT images and to aid physicians in planning both radiotherapy doses and surgery. A panel of 6 experts manually segmented 11 cases of tumors, and the segmentation results were compared exhaustively with: the results provided by a surgical planning tool (AYRA), the segmentations obtained using a radiotherapy treatment planning system (Pinnacle $$^{\textregistered }$$ ), the segmentation results obtained by a group of experts in the delimitation of retroperitoneal tumors and the segmentation results using the algorithm under validation. 11 cases of retroperitoneal tumors were tested. The proposed algorithm provided accurate results regarding the segmentation of the tumor. Moreover, the algorithm requires minimal computational time—an average of 90.5% less than that required when manually contouring the same tumor. A method developed for the semiautomatic selection of retroperitoneal tumor has been validated in depth. AYRA, as well as other surgical and radiotherapy planning tools, could be greatly improved by including this algorithm.

Heart Failure (HF) is a rapidly increasing cardiovascular chronic disease that affects millions of people globally. Lack of proper management of HF patients increases the risk of frailty and other undesirable effects and contributes to loss of independence. The engagement of the HF patient and all actors related to his/her disease management is critical for empowering the patients in achieving sustainable behaviour change, regarding their adherence and compliance. To address this, the concept and the architecture of a mobile co-operative platform are described. The design and development is based on a multi-stakeholder patient centered mHealth ecosystem for HF patients that will facilitate the collaboration of multidisciplinary actors.

An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm’s dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.

HL7 is an international organization that defines standards on health information systems. Most HL7 domain information models are designed according to an own graphic language. All this domain models are based in a unique metamodel. In the last years, many researchers have considered the possibility of using HL7 in the Model Driven Engineering (MDE) context. However, we have identified a weakness: most MDE tools do not support HL7 own model language, but all of them support UML standard model language. The present research area aims to connect HL7 with software analysis through a model-based approach.

An innovative system called VirSSPA that generates personalized patients biomodels in virtual reality and physicals material to plan, support the decision making and guidance of surgical processes is presented. VirSSPA began with a R&D project financed by the Andalusia Department of Health. In this paper the image analysis, segmentation and modeling performed in VirSSPA are explained. Also, a system for surgical knowledge Management was developed. Furthermore, a review of the use of VirSSPA and its applicability in 9 different surgical specialties is shown. VirSSPA has been successfully used in more than 580 real cases. A clinical study of the results obtained in using VirSSPA shows that the complication rate was reduced by 45 % and by two hours in the operating room time in breast microvascular reconstruction by DIEAP free flap.

The vision of implementing FAIR (findable, accessible, interoperable, and reusable) principles in health research over the next years includes a vast, open community of health research institutions and researchers fully engaged in enhancing their knowledge-based economy and their research excellence by adopting the FAIR data guiding principles. We envision high-quality health research and routine care data to be shared and reused in a secure, controlled, and legally compliant environment in both human and machine-readable formats. The idea is to accelerate knowledge discovery while reducing biases and enhancing the strength and quality of the scientific evidence raised by the FAIR community members.